What is the difference between data governance and data stewardship?

difference between data governance and data stewardship

Data governance and data stewardship are two terms that often crop up during discussions of digital initiatives and regulatory compliance. What are the differences between data governance and data stewardship, and how can the implementation of these two concepts by business and IT help to give your company an edge on streamlined access to accurate data?

As its name implies, data governance refers to the policies, rules, and procedures which govern your organization’s data. Data stewardship, on the other hand, is about the monitoring, implementation and enforcement of those policies, rules, and procedures across your business units.

Different strokes

Organizations vary widely, though, in their approaches to data governance and data stewardship, and in how well (or not) they perform on them. Officially, the chief data officer (CDO) — or someone else in IT with a similar title – is usually ultimately responsible for both data governance and data stewardship.

However, while this IT leader holds direct accountability for governance, responsibility for stewardship is also shared by data stewards. In many senses, data stewards act as “glue” between IT and business management in data initiatives, because these folks function in both worlds.

Stewards’ roles are typically organized by data domains. SAS, for example, has pointed to five domain models in use at various organizations:

  • Subject area (such as customer or product)
  • Function (such as customer service or supply chain)
  • Process
  • System where the data originated
  • Project

Data stewards are in charge of making sure that the data in their respective domains is clean and ready for use in corporate projects such as analytics and artificial intelligence (AI), notes Alex Woodie, in a report in Datanami. At the same time, stewards work with “data owners” (who are usually business department heads) and end-users in the desired use of data.

Typically, stewards evaluate requirements and problems with data, and support projects and digitization initiatives like online shops and customer portals as experts for their respective domain.

Some stewards are more technically savvy than others. In some organizations, the role is split across business stewards and technical stewards. Sometimes known as data architects, technical stewards generally work across domains.

Stewards aren’t always that visible

Most large companies do have data stewards, but the role is much less visible at some organizations than others, such as companies in information services and finance. At one major Wall Street firm, for example, the chief data and analytics officer has reportedly overseen some 30 data stewards simultaneously.

Despite the collaboration with IT, however, data stewards must respond to business needs. “For us, data stewardship is about ‘policy enforcement’ but this requires a business acumen, most likely NOT found in IT, nor is it the responsibility of IT for this work,” observes Andrew White, research VP and distinguished analyst at Gartner. 

“If IT ends up doing this work, it is very hard for business to justify ‘staying involved’ with ‘policy setting’ (what we call the role of governance).  And too often history is littered with IT efforts to steward data.”

Data governance needs business involvement, too

Data governance should also be driven by business needs, but that doesn’t always happen, either. “Good data and analytics governance enables faster, smarter decisions. Organizations that want to improve the quality of their data often begin with data and analytics governance projects.  Companies start data and analytics governance initiatives to drive better information behaviors through their policies,” according to a recent report by Gartner.  “However, governance practices continue to be data-oriented rather than business-oriented.”

In a survey, Gartner found that 42 percent of data and analytics leaders do not assess, measure or monitor their data and analytics governance. Beyond that, those who do measure their governance activity focus largely on achieving goals related to regulatory compliance.

With multiple responses permitted, 27 percent of survey participants said they measure against compliance standards set by their organization or the industry at large. Another 18 percent measure for changes in organizational culture and behavior resulting from governance initiatives. Only 15 percent measure against scorecards agreed upon by senior business area leaders. Other types of criteria get measured in smaller numbers.

“CDOs and data and analytics leaders must ensure that their governance initiatives have concrete, measurable metrics that link data and analytics assets and initiatives to business and stakeholder value. For example, tie customer contact data quality to the percentage of customer retention in a specific market segment or percentage of revenue achieved via ecosystem partners,” illustrates the report.

What can happen without governance and stewardship

Clearly, data governance and data stewardship shouldn’t be about compliance only. Here at Strategis, we see many examples of what happened when IT built legacy systems in silos, without data governance, stewardship, and attention paid to business needs. Data quality suffered, and so did the kind of interoperability required today in modern, enterprise-wide data initiatives.

For instance, we are now creating an analytics solution that will be rolled out to frontline store managers and their hierarchy of directors and VPs. Each of these business users is expected to only be able to see their respective portion of the company hierarchy.

We had to talk to more than 10 people just to be able to answer the relationship questions of how a frontline store manager (and their network ID) could be translated to the requisite identifiers used to control data-level security. The financial system uses one set of codes to define people. The timekeeping system uses a different set. The network has its own. Bottom line, it took us several days of tracking down details and will cost the project weeks to develop a solution just to know “Who is this user?” and “What should they have visibility to see?”

Teamwork all the way

If your organization is experiencing issues with data quality and interoperability, it could be well worth your while to reassess your approach to data governance. “Data quality is not solely the job of the IT organization. Data governance work must rally stakeholders to the cause, and IT and the business must be clear on the roles they play. The business decides expectations for data quality, but the business also needs to understand that IT does not own data governance and is not responsible for data quality,” Gartner said in its report.

Data stewards are also a critical component of data quality. “To achieve the greatest benefit, enterprises must properly define the data steward role, select the right individuals to fill the role, and guide the stewards’ behavior in accordance with best practices,” also according to Gartner.

Improved data stewardship might not be enough, just by itself, however. To take data governance to new heights, many enterprises today are also putting together cross-disciplinary teams for the development of new data governance initiatives. These teams are made up of representatives of IT as well as stakeholders such as management, sales, marketing, product development, customer support, professional services, legal, compliance, finance, and more.

Contact the team at Strategis to help you effectively manage your data.